Today’s transportation has intensified the trade and other related activities at considerably fast pace. This has increased the risks to human lives as a result of road accidents. It is, therefore, extremely essential to think of ways and means to perfect the driving skills and acquaint the drivers with the traffic rules as well as to institute a mechanism that assesses the driving skills. So, in this project we have developed a driver profiling system that facilitates the training of drivers to enhance their skills and sense of awareness to reduce the road accidents and also categorizes the drivers based upon their driving skills.
The driver profiling system consists of two main modules i.e. Software & Hardware. The hardware part of the system consists of different vehicle components such as driving seat, steering wheel, a gear assembly, indicator, accelerator and brake pedals etc. These components have switches connected to them to identify any activity on these components. The data coming from all these switches are fed to the micro-controller which in turn serially transmits it to the Software part. The software simulator environment serially receives the data from the micro-controller and controls the vehicle dynamics based on this data. The profiler records the data sets, coming from the micro-controller, and compares it with the standardized patterns of different profiles and categorizes the driver accordingly.
Taking into account the risk mitigation strategies that considers the behavioral characteristics and skills of the driver, the driver profiling system can be extended to generate an early warning of an imminent road crash. The functionality of this system can be further extended in such a way that the driving performances of a particular driver is maintained in a database. This would help the traffic departments to not only assess the quality of the driver but would also help in license issuance. Furthermore, additional functionality can be added into the system which allows the system to take control of the vehicle, if the driver is driving too rashly beyond a certain critical limit.
(Left to Right) Ali Faizan, Umair Arif, Dr. Zahid Halim (Advisor), Rehan Abbasi, Hira Inam (Sitting)
Z. Halim, R. Kalsoom, S. Bashir, and G. Abbas, "Artificial intelligence techniques for driving safety and vehicle crash prediction," Artificial Intelligence Review, Vol. 46, No. 03, 2017, pp. 351–387. [ISSN: 0269-2821, Thomson Reuters JCR 2016, Impact factor 2.627, Springer]
Z. Halim, R. Kalsoom, and A. R. Baig, "Profiling drivers based on driver dependent vehicle driving features," Applied Intelligence, Vol. 44, No. 03, 2016, pp. 645-664. [ISSN: 0924-669X, Thomson Reuters JCR 2017, Impact factor 1.904, Springer]